Pub Date : 2024-03-19DOI: 10.1108/jpif-09-2023-0084
G. Morri, Fan Yang, Federico Colantoni
PurposeThe aim of this research paper is to analyze the connection between ESG performance and financial performance within the real estate sector. By focusing on ESG ratings and pillar scores as proxies for ESG performance, the study investigates how these factors impact both profitability and market indicators.Design/methodology/approachWith data sourced from over 680 publicly listed real estate companies, the research employs a fixed effects regression model to analyze the findings. By utilizing this method, the study can assess the impact of governance, environmental and social factors on both the accounting and market performance of real estate companies.FindingsThe outcomes of this study underscore a link between sustainability, particularly environmental aspects and financial performance. However, the study also reveals a contrasting result: governance factors are associated with adverse financial outcomes. Nevertheless, it is important to highlight the limitations as the results present a mixed picture with limited significant findings.Practical implicationsCompanies should prioritize improvements in environment to boost profitability, while they should carefully consider the costs and benefits associated with enhancing their governance structure.Originality/valueBy focusing on this industry and adopting a global perspective, the study addresses a gap in the literature. The research’s innovative approach to utilizing ESG ratings and pillar scores as proxies for ESG performance enhances its originality. Furthermore, the research’s identification of the differing impacts of environmental and governance factors on financial outcomes add novel perspectives to the discourse.
{"title":"Green investments, green returns: exploring the link between ESG factors and financial performance in real estate","authors":"G. Morri, Fan Yang, Federico Colantoni","doi":"10.1108/jpif-09-2023-0084","DOIUrl":"https://doi.org/10.1108/jpif-09-2023-0084","url":null,"abstract":"PurposeThe aim of this research paper is to analyze the connection between ESG performance and financial performance within the real estate sector. By focusing on ESG ratings and pillar scores as proxies for ESG performance, the study investigates how these factors impact both profitability and market indicators.Design/methodology/approachWith data sourced from over 680 publicly listed real estate companies, the research employs a fixed effects regression model to analyze the findings. By utilizing this method, the study can assess the impact of governance, environmental and social factors on both the accounting and market performance of real estate companies.FindingsThe outcomes of this study underscore a link between sustainability, particularly environmental aspects and financial performance. However, the study also reveals a contrasting result: governance factors are associated with adverse financial outcomes. Nevertheless, it is important to highlight the limitations as the results present a mixed picture with limited significant findings.Practical implicationsCompanies should prioritize improvements in environment to boost profitability, while they should carefully consider the costs and benefits associated with enhancing their governance structure.Originality/valueBy focusing on this industry and adopting a global perspective, the study addresses a gap in the literature. The research’s innovative approach to utilizing ESG ratings and pillar scores as proxies for ESG performance enhances its originality. Furthermore, the research’s identification of the differing impacts of environmental and governance factors on financial outcomes add novel perspectives to the discourse.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1108/jpif-02-2024-0021
Peter Papadakos
PurposeThe intent of this Practice Briefing is to provide clarity on drivers of property pricing in a changing economic environment. The principal basis of this analysis is to investigate how properties have been priced relative to interest rates over the long haul. Such an insight may help investors navigate the world of property investment in a post zero interest-rate policy (ZIRP) world.Design/methodology/approachThis practice briefing is an overview of the role of economic drivers in pricing property in different economic eras pre- and post-ZIRP. It looks at returns over time relative to risk criteria and growth.FindingsThis briefing is a review of property pricing and its relationship to economic drivers and discusses the concept of return premiums as a market indicator to spot under/over-priced property assets in the market.Practical implicationsThis briefing considers the implications of identifying salient and pertinent market indicators over time as bellweathers for property pricing. Good property investment is grounded in understanding when assets are under and overpriced relative to investors’ expectations of growth and returns going forward. An understanding of markets and the current indicators thereof can provide investors with insights into those criteria.Originality/valueThis provides guidance on how to interpret markets and get an understanding of property pricing over time.
{"title":"Practice Briefing Market indications and property returns in the UK","authors":"Peter Papadakos","doi":"10.1108/jpif-02-2024-0021","DOIUrl":"https://doi.org/10.1108/jpif-02-2024-0021","url":null,"abstract":"PurposeThe intent of this Practice Briefing is to provide clarity on drivers of property pricing in a changing economic environment. The principal basis of this analysis is to investigate how properties have been priced relative to interest rates over the long haul. Such an insight may help investors navigate the world of property investment in a post zero interest-rate policy (ZIRP) world.Design/methodology/approachThis practice briefing is an overview of the role of economic drivers in pricing property in different economic eras pre- and post-ZIRP. It looks at returns over time relative to risk criteria and growth.FindingsThis briefing is a review of property pricing and its relationship to economic drivers and discusses the concept of return premiums as a market indicator to spot under/over-priced property assets in the market.Practical implicationsThis briefing considers the implications of identifying salient and pertinent market indicators over time as bellweathers for property pricing. Good property investment is grounded in understanding when assets are under and overpriced relative to investors’ expectations of growth and returns going forward. An understanding of markets and the current indicators thereof can provide investors with insights into those criteria.Originality/valueThis provides guidance on how to interpret markets and get an understanding of property pricing over time.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"23 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.1108/jpif-12-2023-0107
David Parker
PurposeTo explore value vs worth in the context of compulsory acquisition.Design/methodology/approachAnalysis of statutory environment within the context of valuation theory.FindingsValue and worth could be reconciled by redefining special value in Act.Research limitations/implicationsPublic policy amendment.Practical implicationsPublic policy amendment.Social implicationsFacilitate just compensation.Originality/valueTopical issue in New South Wales, where massive compulsory acquisition programme underway to facilitate infrastructure development.
{"title":"Real Estate InsightsCompensation for compulsory acquisition – value vs worth","authors":"David Parker","doi":"10.1108/jpif-12-2023-0107","DOIUrl":"https://doi.org/10.1108/jpif-12-2023-0107","url":null,"abstract":"PurposeTo explore value vs worth in the context of compulsory acquisition.Design/methodology/approachAnalysis of statutory environment within the context of valuation theory.FindingsValue and worth could be reconciled by redefining special value in Act.Research limitations/implicationsPublic policy amendment.Practical implicationsPublic policy amendment.Social implicationsFacilitate just compensation.Originality/valueTopical issue in New South Wales, where massive compulsory acquisition programme underway to facilitate infrastructure development.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"25 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1108/jpif-09-2023-0083
Marcelo Cajias, Anna Freudenreich
PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.
{"title":"What are tenants demanding the most? A machine learning approach for the prediction of time on market","authors":"Marcelo Cajias, Anna Freudenreich","doi":"10.1108/jpif-09-2023-0083","DOIUrl":"https://doi.org/10.1108/jpif-09-2023-0083","url":null,"abstract":"PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"50 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1108/jpif-09-2023-0083
Marcelo Cajias, Anna Freudenreich
PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.
{"title":"What are tenants demanding the most? A machine learning approach for the prediction of time on market","authors":"Marcelo Cajias, Anna Freudenreich","doi":"10.1108/jpif-09-2023-0083","DOIUrl":"https://doi.org/10.1108/jpif-09-2023-0083","url":null,"abstract":"PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"50 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1108/jpif-02-2024-218
Nick French
{"title":"Editorial: Issues, challenges and unknowns of Artificial Intelligence in the world of real estate","authors":"Nick French","doi":"10.1108/jpif-02-2024-218","DOIUrl":"https://doi.org/10.1108/jpif-02-2024-218","url":null,"abstract":"","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"168 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1108/jpif-02-2024-218
Nick French
{"title":"Editorial: Issues, challenges and unknowns of Artificial Intelligence in the world of real estate","authors":"Nick French","doi":"10.1108/jpif-02-2024-218","DOIUrl":"https://doi.org/10.1108/jpif-02-2024-218","url":null,"abstract":"","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"244 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-30DOI: 10.1108/jpif-10-2023-0095
Rebecca Restle, Marcelo Cajias, Anna Knoppik
PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.
{"title":"The price of clean air – quantifying air pollution exposure in real estate decisions","authors":"Rebecca Restle, Marcelo Cajias, Anna Knoppik","doi":"10.1108/jpif-10-2023-0095","DOIUrl":"https://doi.org/10.1108/jpif-10-2023-0095","url":null,"abstract":"PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139591445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1108/jpif-12-2023-0108
C. Lizieri
PurposeThe aim of this Real Estate Insight is to comment upon commercial real estate research. Much of the current research on commercial real estate sits in academic silos, constrained by disciplinary boundaries and rejecting insights from other areas. This can lead to an impoverished understanding of the processes and practices that drive market behaviour.Design/methodology/approachThis Real Estate Insight, through the lens of history, draws on insights from a century earlier and, in particular, from the work of Frank Ramsey; the paper argues that market behaviour is shaped by the role of key actors and persistent beliefs which need to be accounted for in our models of market practice.FindingsThe paper argues that current research paradigms need to accommodate agency explicitly into existing models and that real estate research will benefit immensely if researcher were more open in seeking ideas from outside the real estate field and to be more open to external ideas and concepts.Practical implicationsThe paper suggests that property research needs to be more embracing of other academic disciplines to develop a full understanding of the numerous and various drivers within commercial real estate markets.Originality/valueThis is a review of how beliefs impact upon commercial real estate markets. As with many things, history can help researchers today get a broader and more appropriate perspective on market drivers and how they affect decision-making.
{"title":"Real Estate Insights: Individuals, beliefs and decision-making in commercial real estate markets: some reflections from the past","authors":"C. Lizieri","doi":"10.1108/jpif-12-2023-0108","DOIUrl":"https://doi.org/10.1108/jpif-12-2023-0108","url":null,"abstract":"PurposeThe aim of this Real Estate Insight is to comment upon commercial real estate research. Much of the current research on commercial real estate sits in academic silos, constrained by disciplinary boundaries and rejecting insights from other areas. This can lead to an impoverished understanding of the processes and practices that drive market behaviour.Design/methodology/approachThis Real Estate Insight, through the lens of history, draws on insights from a century earlier and, in particular, from the work of Frank Ramsey; the paper argues that market behaviour is shaped by the role of key actors and persistent beliefs which need to be accounted for in our models of market practice.FindingsThe paper argues that current research paradigms need to accommodate agency explicitly into existing models and that real estate research will benefit immensely if researcher were more open in seeking ideas from outside the real estate field and to be more open to external ideas and concepts.Practical implicationsThe paper suggests that property research needs to be more embracing of other academic disciplines to develop a full understanding of the numerous and various drivers within commercial real estate markets.Originality/valueThis is a review of how beliefs impact upon commercial real estate markets. As with many things, history can help researchers today get a broader and more appropriate perspective on market drivers and how they affect decision-making.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"40 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1108/jpif-05-2023-0046
Deborah Leshinsky, Stanley McGreal, Paloma Taltavull, Anthony McGough
PurposeIn Family Law Court decisions in Australia, following divorce, the female party is frequently disadvantaged financially in the long term. This paper provides a critical assessment of valuation evidence as a data source in research and discusses valuation accuracy, valuation variation and valuation bias, as well as the Australian family court system and the role of valuers as expert witnesses. In particular, valuation in family law, as it relates to gender inequality, is discussed. The study aims to determine whether the current system of valuation in the Family Law Courts disadvantages women. This paper was important to reveal information that stakeholders in family law cases use on a day-to-day basis.Design/methodology/approachA database of 658 cases was developed and analysed to examine the influence of valuations of the matrimonial home provided by both the male and female parties on the final decision of the court.FindingsFindings showed that valuations from the female party had marginally more influence on the outcome. However, financial disadvantages for the female party persist despite this. This raises several questions for future research, regarding reasons for this persistent disadvantage.Research limitations/implicationsResearch limitations included a time-consuming process.Practical implicationsFurther researchers can use the findings from this paper to further research.Social implicationsSocial implications include the ability of the research to impact on society. In this regard, it was the matrimonial home in relation to divorce proceedings.Originality/valueThe originality of this paper stems from the analysis of a database that was created from a large number of cases from Austlii database family law cases.
{"title":"Property valuations decided through the court system in family law separation in Australia","authors":"Deborah Leshinsky, Stanley McGreal, Paloma Taltavull, Anthony McGough","doi":"10.1108/jpif-05-2023-0046","DOIUrl":"https://doi.org/10.1108/jpif-05-2023-0046","url":null,"abstract":"PurposeIn Family Law Court decisions in Australia, following divorce, the female party is frequently disadvantaged financially in the long term. This paper provides a critical assessment of valuation evidence as a data source in research and discusses valuation accuracy, valuation variation and valuation bias, as well as the Australian family court system and the role of valuers as expert witnesses. In particular, valuation in family law, as it relates to gender inequality, is discussed. The study aims to determine whether the current system of valuation in the Family Law Courts disadvantages women. This paper was important to reveal information that stakeholders in family law cases use on a day-to-day basis.Design/methodology/approachA database of 658 cases was developed and analysed to examine the influence of valuations of the matrimonial home provided by both the male and female parties on the final decision of the court.FindingsFindings showed that valuations from the female party had marginally more influence on the outcome. However, financial disadvantages for the female party persist despite this. This raises several questions for future research, regarding reasons for this persistent disadvantage.Research limitations/implicationsResearch limitations included a time-consuming process.Practical implicationsFurther researchers can use the findings from this paper to further research.Social implicationsSocial implications include the ability of the research to impact on society. In this regard, it was the matrimonial home in relation to divorce proceedings.Originality/valueThe originality of this paper stems from the analysis of a database that was created from a large number of cases from Austlii database family law cases.","PeriodicalId":506679,"journal":{"name":"Journal of Property Investment & Finance","volume":"29 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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